NEAFWA 2018

AUTHORS: Cathleen Balantic, Vermont Cooperative Fish and Wildlife Research Unit, University of Vermont; Therese Donovan, U.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research Unit, University of Vermont; Jonathan Katz, Vermont Cooperative Fish & Wildlife Research Unit; Mark Massar, U.S. Bureau of Land Management

ABSTRACT. Acoustic recordings of the environment can produce species presence-absence data for characterizing populations of sound-producing wildlife over vast spatial scales. If a species is present on site but does not vocalize during a scheduled audio recording session, researchers may incorrectly conclude that the species is absent (‘false negative’). The risk of false negatives is compounded when audio devices don’t record continuously and must be manually scheduled to operate at pre-selected times of day, particularly when research programs target multiple focal species with vocal availability that varies across temporal and environmental conditions. In the R package AMMonitoR, we developed a temporally-adaptive acoustic sampling algorithm to maximize detection probabilities for a suite of focal species amid sampling constraints. The algorithm combines user-supplied species vocalization models with site-specific weather forecasts to set an optimized sampling schedule for the following day. To test our algorithm, we simulated hourly vocalization probabilities for a suite of focal species in a hypothetical monitoring area for the year 2016. We conducted a factorial experiment that sampled from the 2016 acoustic environment to compare the probability of acoustic detection by a fixed (stationary) schedule vs. a temporally-adaptive optimized schedule under several sampling efforts and monitoring durations. We found that over the course of a study season, the probability of acoustically capturing a focal species at least once via automated acoustic monitoring is higher (and acoustic capture occurs earlier in the season) when using the temporally-adaptive optimized schedule as compared to a fixed schedule. The advantages of a temporally-adaptive optimized acoustic sampling schedule are magnified when a study duration is short, sampling effort is low, and/or species vocal availability is minimal. This methodology thus offers new possibilities to the existing paradigms for adaptive wildlife sampling and acoustic monitoring, potentially allowing research programs to maximize sampling efforts amid constraints.